Python 3.11.0 | packaged by conda-forge | (main, Jan 16 2023, 14:12:30) [MSC v.1916 64 bit (AMD64)]
Type 'copyright', 'credits' or 'license' for more information
IPython 8.12.2 -- An enhanced Interactive Python. Type '?' for help.
import os
from os.path import join
import sys
from functools import partial
sys.path.append(os.path.join(os.getcwd(), '..')) #adds directory below as valid path
from datetime import datetime, timedelta
dateformat = "%H-%M-%S"
from collections import deque
import traceback
from multiprocessing import Pool
from tqdm import tqdm
import scipy.constants as spc
from lmfit import Model, create_params
from scipy.integrate import odeint
from scipy.optimize import curve_fit
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
plt.rcParams['axes.grid'] = True
plt.rcParams['grid.linestyle'] = '--'
from MT_class_PID_new import MTdataHost
from global_folder.myplotsty import *
from global_folder.my_helpers import *
PUMP_FREQUENCY = 384228.6
REPUMP_FREQUENCY = 384228.6 + 6.56
SAMPLE_RATE = 2000
FREQVSVOLT = 221.0
FREQVSCURR = 1.13
# TODO: find a better place for this
EXP_FOLDER =r'C:\Users\svars\OneDrive\Desktop\UBC Lab\CATExperiment\CATMeasurements'
MEASURE_FOLDER = os.path.join(EXP_FOLDER, 'testPArun16')
WDATA_FOLDER =os.path.join(MEASURE_FOLDER, 'testPArun16.csv')
# TODO: maybe make a run analysis class out of this?
def dump():
collect_plots(MEASURE_FOLDER, os.path.join(MEASURE_FOLDER, 'collected_plots'), 'deloadPhase.png')
#*-----------------------
#* SINGLE RUN
#*-----------------------
MEASURE_FOLDER = os.path.join(EXP_FOLDER, 'testPArun9')
df = get_data_frame(MEASURE_FOLDER)
df.drop(columns=['betaPAErr'], inplace=True)
df.dropna(inplace=True)
#freqs = plot_results(df, 384201., save_folder=MEASURE_FOLDER)
max_freq = 384182.5
data = df
freqs = ((max_freq-PUMP_FREQUENCY)-(data['tempV']-data['tempV'].min())*FREQVSVOLT- (data['currV']-df['currV'].min())*FREQVSCURR)
fig, ax = plt.subplots()
plot_spline_fit(ax=ax, x=freqs, y=data['ratio'], yerr=data['ratioErr'],
s=0.0, save_folder=MEASURE_FOLDER,
mfc='red', color='black',
title='')
plt.show()
plt.close()
betaPAs = [a for a,b in sorted(zip(data['betaPA'], freqs), key=lambda pair:pair[1])]
freqs = sorted(freqs)
plt.plot(freqs, betaPAs, 'o-', ms=5, label="")
plt.legend()
plt.xlabel(r'$\Delta$ (GHz)')
plt.ylabel(r'$\beta_{\mathrm{eff}}$ ')
#plt.savefig(join(MEASURE_FOLDER, 'betaVsFreq.png'), dpi=200)
plt.title(f"2-body Decay Plot {''} ", **titledict)
plt.show()
plt.close()
# *-----------------------
# * MULTIPLE RUN COMPARISON
# *-----------------------
names = ['wide1', 'medium1', 'small1', 'smallest1']
folders = [os.path.join(EXP_FOLDER,'RepumpTrapDepthEffect', path) for path in names ]
dfs = [get_data_frame(measure_folder) for measure_folder in folders]
labels = names
max_freqs = [384182.6]*len(dfs)
zipped_data = list(zip(dfs, max_freqs))
fig, ax = plt.subplots()
for i, (df, max_freq) in enumerate(zipped_data[:]):
data = df.dropna()
freqs = ((max_freq-PUMP_FREQUENCY)-(data['tempV']-data['tempV'].min())*FREQVSVOLT- (data['currV']-data['currV'].min())*FREQVSCURR)
ax=plot_spline_fit(ax, x=freqs, y=data['ratio'], yerr=data['ratioErr'], scolor=f'C{i}', mfc=f'C{i}',color=f'C{i}', s=0.00, ms=5, label=labels[i])
plt.legend()
fig, ax = plt.subplots()
for i, (df, max_freq) in enumerate(zipped_data[:]):
data = df.dropna()
freqs = ((max_freq-PUMP_FREQUENCY)-(data['tempV']-data['tempV'].min())*FREQVSVOLT- (data['currV']-data['currV'].min())*FREQVSCURR)
betaPAs = [a for a,b in sorted(zip(data['betaPA'], freqs), key=lambda pair:pair[1])]
freqs = sorted(freqs)
plt.plot(freqs, betaPAs, 'o-', ms=5, label=labels[i])
plt.legend()
#*-----------------------
#* PARSING WAVEMETER DATA
#*-----------------------
MEASURE_FOLDER = os.path.join(EXP_FOLDER, 'testPArun14')
WDATA_FOLDER =os.path.join(MEASURE_FOLDER, 'testPArun14.csv')
freq_data, max_freq, min_freq = add_wavemeter_data('', WDATA_FOLDER)
data = freq_data[:]
levels = staircase_fit(data)
data = get_data_frame(MEASURE_FOLDER)
data.dropna(inplace=True)
freqs = ((max_freq)-(data['tempV']-data['tempV'].min())*FREQVSVOLT- (data['currV']-data['currV'].min())*FREQVSCURR)
plt.plot(freqs)
#*-----------------------
#* MEGA_RUN
#*-----------------------
MEASURE_FOLDER = os.path.join(EXP_FOLDER, 'MegaRuns', 'testPArunMega3')
df = get_data_frame(MEASURE_FOLDER,
plot=False,
cache_all=True)
df.drop(columns=['betaPAErr'], inplace=True)
df.dropna(inplace=True)
df = df[df['ratio']<1.2]
groupbyKey = 'pump_reference'
titleKey = 'pump_AOM_freq'
df_grouped = df.groupby(by=groupbyKey)
min_ratios = df_grouped['ratio'].min()
groups = dict(list(df_grouped))
dfs = [df for df in groups.values()]
# plotting ratio vs freq
max_freqs = [384182.5]*len(dfs)
zipped_data = list(zip(dfs, max_freqs))
fig, ax = plt.subplots()
for i, (df, max_freq) in enumerate(zipped_data[:]):
data = df
freqs = ((max_freq-PUMP_FREQUENCY)-(data['tempV']-data['tempV'].min())*FREQVSVOLT- (data['currV']-data['currV'].min())*FREQVSCURR)
ax=plot_spline_fit(ax, x=freqs, y=data['ratio'], yerr=data['ratioErr'],scolor=f'C{i}', mfc=f'C{i}',color=f'C{i}', s=0.0, ms=5, figsize=(10, 10), label=f"{groupbyKey} = { data.iloc[10][groupbyKey] :.2f}", linewidth=2.5)
plt.legend()
plt.savefig(os.path.join(MEASURE_FOLDER, 'lossFeatures.png'))
plt.title(f'Loss Features, {titleKey} = {data[titleKey].mean():.2f}', **titledict)
plt.show()
plt.close()
#---------------------------------------------------
# x = [df[groupbyKey].mean() for df in dfs]
# y = [(df['ratio'].max() - df['ratio'].min()) for df in dfs]
# plt.plot( x, y ,'-o')
# plt.xlabel(groupbyKey)
# plt.ylabel(r'SNR $ = V_{ss, off} - V_{ss, on}$ ')
# plt.title(f'SNR Plot, {titleKey} = {df[titleKey].mean():.2f}', **titledict)
# plt.show()
# plt.savefig(join(MEASURE_FOLDER, 'SNRplot.png'), dpi=200)
# plt.close()
#---------------------------------------------------
for i, df in enumerate(dfs[:]):
data = df
freqs = ((max_freqs[0]-PUMP_FREQUENCY)-(data['tempV']-data['tempV'].min())*FREQVSVOLT- (data['currV']-data['currV'].min())*FREQVSCURR)
betaPAs = [a for a,b in sorted(zip(data['betaPA'], freqs), key=lambda pair:pair[1])]
freqs = sorted(freqs)
plt.plot(freqs, betaPAs, 'o-', ms=5, label=f"{groupbyKey}={data.iloc[10][groupbyKey]:.2f}")
plt.legend()
plt.xlabel(r'$\Delta$ (GHz)')
plt.ylabel(r'$\beta_{\mathrm{eff}}$ ')
plt.savefig(join(MEASURE_FOLDER, 'betaVsFreq.png'), dpi=200)
plt.title(f'2-body Decay Plot, {titleKey} = {df[titleKey].mean():.2f}', **titledict)
plt.show()
plt.close()
#*-----------------------
#* MULTIPLE MEGARUN
#*-----------------------
folders = [os.path.join(EXP_FOLDER, 'MegaRuns', path ) for path in ['testPArunMega7', 'testPArunMega8']]
dfs_mega = [get_data_frame(measure_folder, cache_all=True) for measure_folder in folders]
groupbyKey = 'pump_reference'
titleKey = 'pump_AOM_freq'
dfs_grouped = [df_mega.groupby(by=groupbyKey) for df_mega in dfs_mega]
min_ratios = [df_grouped['ratio'].min() for df_grouped in dfs_grouped]
groupss = [dict(list(df_grouped)) for df_grouped in dfs_grouped]
dfs = [ [df for df in groups.values()] for groups in groupss]
for row in dfs:
data = row[3]
freqs = ((384182.5-PUMP_FREQUENCY)-(data['tempV']-data['tempV'].min())*FREQVSVOLT- (data['currV']-data['currV'].min())*FREQVSCURR)
betaPAs = [a for a,b in sorted(zip(data['betaPA'], freqs), key=lambda pair:pair[1])]
freqs = sorted(freqs)
plt.plot(freqs, betaPAs, 'o-', ms=5, label=f"{titleKey}={data.iloc[10][titleKey]:.2f}")
plt.title(f'{groupbyKey} = {data.iloc[10][groupbyKey]:.2f}')
plt.legend()
#*-----------------------
#* FULL RUNS
#*-----------------------
MEASURE_FOLDER = os.path.join(EXP_FOLDER, 'CATrunOct4')
df = get_data_frame(MEASURE_FOLDER)
df.drop(columns='betaPAErr', inplace=True)
df.dropna(inplace=True)
dfc = df.copy()
df_grouped = df.groupby(by=['pump_reference', 'pump_AOM_freq'])
groups = dict(list(df_grouped))
dfs = [df for df in groups.values()]
max_freqs = [384394.7]*len(dfs)
zipped_data = list(zip(dfs, max_freqs))
num1 = 3
fig1, ax1s = plt.subplots(num1) # detuning
fig1b, ax1sb = plt.subplots(num1)
fig1size = (8, num1*6)
fig1bsize= fig1size
fig1b.set_size_inches(fig1bsize)
num2 = 2
fig2, ax2s = plt.subplots(num2) # pump_reference
fig2b, ax2sb = plt.subplots(num2)
fig2size = (8, num2*6)
fig2bsize= fig2size
fig2b.set_size_inches(fig2bsize)
for i, (df, max_freq) in enumerate(zipped_data[:]):
j1 = i%num1
j2 = i//num1
data = df.dropna()
data = data[data['ratio'] < 1.3]
freqs = ((max_freq-PUMP_FREQUENCY)-(data['tempV']-data['tempV'].min())*FREQVSVOLT- (data['currV']-0.0)*FREQVSCURR)
pref = data['pump_reference'].mean()
detuning = 180-2*data['pump_AOM_freq'].mean()
print(pref, detuning)
ax1s[j1] = plot_spline_fit(ax1s[j1],
x=freqs, y=data['ratio'], yerr=data['ratioErr']
,scolor=f'C{j2}', mfc=f'C{j2}',color=f'C{j2}'
,s=0.0, ms=5,linewidth=1.5, figsize=fig1size
,label=f"Pump Amplitude = {pref:.2f}", fig=fig1)
ax1s[j1].set_title(f"Detuning = {detuning:.2f}", **titledict)
ax1s[j1].legend()
ax2s[j2] = plot_spline_fit(ax2s[j2],
x=freqs, y=data['ratio'], yerr=data['ratioErr'],
scolor=f'C{j1}', mfc=f'C{j1}',color=f'C{j1}',
s=0.0, ms=5, linewidth=1.5, figsize=fig2size,
label=f"Detuning = {detuning:.2f}", fig=fig2)
ax2s[j2].set_title(f"Pump Amplitude = { pref:.2f}", **titledict)
ax2s[j2].legend()
betaPAs = [a for a,b in sorted(zip(data['betaPA'], freqs), key=lambda pair:pair[1])]
freqs = sorted(freqs)
ax1sb[j1].plot(freqs, betaPAs, 'o-', color=f'C{j2}', ms=5, label=f"Pump Amplitude = {pref:.2f}")
ax1sb[j1].set_xlabel(r'$\Delta$ (GHz)')
ax1sb[j1].set_ylabel(r'$\beta_{\mathrm{eff}}$ ')
ax1sb[j1].legend()
ax1sb[j1].set_title(f"Detuning = {detuning:.2f}", **titledict)
ax2sb[j2].plot(freqs, betaPAs, 'o-',color=f'C{j1}', ms=5, label=f"Detuning = {detuning:.2f}")
ax2sb[j2].set_xlabel(r'$\Delta$ (GHz)')
ax2sb[j2].set_ylabel(r'$\beta_{\mathrm{eff}}$ ')
ax2sb[j2].legend()
ax2sb[j2].set_title(f"Pump Amplitude = { pref:.2f}", **titledict)
fig1.tight_layout()
fig1b.tight_layout()
fig2.tight_layout()
fig2b.tight_layout()
fig1.savefig(os.path.join(MEASURE_FOLDER, 'lossFeaturesDet.png'))
plt.show()
plt.close()
fig1b.savefig(os.path.join(MEASURE_FOLDER, '2bodyDet.png'))
plt.show()
plt.close()
fig2.savefig(os.path.join(MEASURE_FOLDER, 'lossFeaturesPampl.png'))
plt.show()
plt.close()
fig2b.savefig(os.path.join(MEASURE_FOLDER, '2BodyPampl.png'))
plt.show()
plt.close()
SNRdata = df_grouped['ratio'].max() - df_grouped['ratio'].min()
SNRdf = SNRdata.reset_index()
SNRdf.columns = ['pump_reference', 'pump_AOM_freq', 'SNR']
pivot_table = SNRdf.pivot('pump_reference', 'pump_AOM_freq', 'SNR')
xticklabels = [f'{180-2*x:.2f}' for x in pivot_table.columns]
yticklabels = [f'{y:.2f}' for y in pivot_table.index]
sns.heatmap(pivot_table, annot=True, fmt='.2f', xticklabels=xticklabels, yticklabels=yticklabels)
plt.xlabel("Detuning (MHz)")
plt.ylabel("Pump Reference")
plt.grid()
plt.savefig(os.path.join(MEASURE_FOLDER, 'heatmap.png'))
plt.show()
plt.close()
# fig, ax = plt.subplots()
# for i, (df, max_freq) in enumerate(zipped_data[:]):
# data = df.dropna()
# freqs = ((max_freq-PUMP_FREQUENCY)-(data['tempV']-data['tempV'].min())*FREQVSVOLT- (data['currV']-data['currV'].min())*FREQVSCURR)
# ax=plot_spline_fit(ax, x=freqs, y=data['ratio'], yerr=data['ratioErr'],scolor=f'C{i}', mfc=f'C{i}',color=f'C{i}', s=0.0, ms=5, figsize=(10, 10), linewidth=2.5)
# plt.title(f"Pump Amplituide = { df.iloc[10]['pump_reference'] :.2f}, \
# sDetuning = { 180-2*df.iloc[10]['pump_AOM_freq'] :.2f}", **titledict)
# plt.legend()
# plt.savefig(os.path.join(MEASURE_FOLDER, f'lossFeatures{i}.png'))
# plt.show()
# plt.close()
# fig, ax = plt.subplots()
def freq_misc():
WDATA_FOLDER = r'C:\Users\svars\OneDrive\Desktop\UBC Lab\CATExperiment\CATMeasurements\CATcurrTestrun3.csv'
freq_data = add_wavemeter_data('', WDATA_FOLDER)
levels = staircase_fit(freq_data[0], peak_height=0.2, distance=50, data_offset=1, window_size=1)
plt.close()
x = np.linspace(0, 4.9, 25)
y = levels
plt.plot(levels, '-o')
plt.title('Levels plot')
m, b, fit_line = my_linear_fit(x, y)
def save_fit_results(run_path, plot=False, bkfile=False,
CATbaseline=True, MOTbaseline=True,
initRFit=True, loadFit=True,deloadFit=True,reloadFit=True,
storeFitResults=True):
filename = os.path.join(run_path, 'data.csv')
bkfilename = os.path.join(run_path, 'data_oldPD.csv')
settingsname = os.path.join(run_path, 'Settings.txt')
dataHost = MTdataHost(SAMPLE_RATE)
dataHost.loadCATdata(fileName=filename, settingsName=settingsname)
if bkfile:
dataHost.CATbackgroundData(bkfilename)
#dataHost.setAllCAT(0.002)
if CATbaseline:
dataHost.setCATbaseline(0.002)
if MOTbaseline:
dataHost.setBaseline(0.002)
if loadFit:
dataHost.setLoading(0.002)
if initRFit and loadFit:
dataHost.initFit, dataHost.initX = dataHost.setInitialLoad(0.002)
if deloadFit:
dataHost.setDeloading(0.002)
dataHost.plotDeloadFit(run_path)# TODO: currently just stores the deloading times and voltages
if reloadFit:
dataHost.setReloadVolt(0.002)
if CATbaseline and MOTbaseline and loadFit and reloadFit:
# steady state ratio fraction
dataHost.ratio = dataHost.reloadVolt / dataHost.motSS
dataHost.ratioErr = dataHost.ratio * ((dataHost.reloadVoltErr/dataHost.reloadVolt)**2 + (dataHost.motSSErr/dataHost.motSS)**2)**(0.5)
# if abs(dataHost.ratioErr / dataHost.ratio) > 0.1:
# dataHost.ratioErr = abs(0.015*dataHost.ratio)
if dataHost.ratioErr < 0.001:
dataHost.ratioErr = 0.001
if dataHost.ratio < 0:
dataHost.ratio = 0
# TODO: this information is useless
print('File loaded: RFmin = {} MHz, t_mt = {:.3f} s.'.format(dataHost.settings['fmin'], dataHost.settings['wait_mtrap']))
resultDict = dataHost.getResults(run_path, store=storeFitResults)
if plot:
dataHost.storeFits(run_path, combined=True, separate=True)
return resultDict, dataHost.settings
def get_timestamp(run_path):
timestamp = datetime.strptime(os.path.split(run_path)[-1].split('_')[0], dateformat)
return timestamp
def extract_fit(run_path, plot=True, cache_failed=True, cache_all=True, **kwargs):
"""Gather relevant data from each measurement run
Args:
run_path : absolute path to the run directory
plot (bool, optional): plot fits. Defaults to True.
cache_failed (bool, optional): Cache failed fits. If false, refit. Doesn't refit non-failed fits. Defaults to True.
cache_all (bool, optional): If false, ignore any cached fit_results. Defaults to True.
Returns:
a 3-tuple (fit_results, settings, timestamp)
"""
fit_results, settings, timestamp = {}, {}, None
if not os.path.isdir(run_path):
return fit_results, settings, timestamp # directory is not a run directory
try:
timestamp = get_timestamp(run_path)
# TODO: specify which error to catch
except Exception as e:
print("Error extracting timestamp from: ", run_path)
print(traceback.format_exc())
MAT_fit_cache_path = os.path.join(run_path, 'resultDict.txt')
if not os.path.exists(MAT_fit_cache_path) or not cache_all:
try:
fit_results, settings = save_fit_results(run_path, plot=plot, **kwargs)
except Exception as e:
print(traceback.format_exc())
print("Fitting ERROR at ", os.path.basename(run_path), '\n')
with open(MAT_fit_cache_path, 'w') as f:
f.write(str('MAT fit failed'))
else:
print("Accessing cached results from :", os.path.basename(run_path))
fit_results = open(MAT_fit_cache_path, 'r').read()
if fit_results == 'MAT fit failed':
if not cache_failed:
# fit regardless of cached result
try:
fit_results, settings = save_fit_results(run_path, plot=plot)
except Exception as e:
print(traceback.format_exc())
print("Fitting ERROR at ", os.path.basename(run_path), '\n')
with open(MAT_fit_cache_path, 'w') as f:
f.write(str('MAT fit failed'))
else:
print("Failed fit at :", os.path.basename(run_path))
fit_results = {}
else:
fit_results = eval(open(MAT_fit_cache_path, 'r').read())
settingsname = os.path.join(run_path, 'Settings.txt')
settings = eval(open(settingsname, 'r').read())
return fit_results, settings, timestamp
def get_row(run_path, **kwargs):
fit_results, settings, timestamp = extract_fit(run_path, **kwargs)
row = {**fit_results, **settings, **{'timestamp':timestamp}}
return row
def get_data_frame(data_dir, parallel=True, in_process_run=False, **kwargs):
run_path_arr = []
rows = []
for relative_path in os.listdir(data_dir):
run_path_arr.append(os.path.join(data_dir, relative_path))
if in_process_run:
run_path_arr.pop()
run_path_arr = sorted(run_path_arr)
if parallel:
with Pool(4) as p:
rows = list(tqdm(p.imap(partial(get_row, **kwargs), run_path_arr), total=len(run_path_arr)))
else:
for run_path in tqdm(run_path_arr):
rows.append(get_row(run_path, **kwargs))
return pd.DataFrame.from_dict(rows)
def add_wavemeter_data(df, wmeter_csv_path, window_size=100, num_rows=50):
"""Extract unique frequnecy values from wavemeter data
Returns:
unique_levels (list): unique frequency values in wavemeter data
"""
# TODO: modify dataframe in place with frequency data
wdata = pd.read_csv(wmeter_csv_path, skiprows=2)
wdata.dropna(inplace=True)
freq_data = np.array(wdata.iloc[:, 0])
try:
freq_data = np.array([float(item) for item in freq_data if item.replace('.','').isdigit()])
except Exception as e:
print(e)
max_freq = freq_data.max()
min_freq = freq_data.min()
return freq_data, max_freq, min_freq
def plot_results(ax, dfs, max_freq, min_freq=0.0, mfc='red', fmt='o', ms=5, save_folder=False, xscale=1.0, yscale=1.0, **kwargs):
FREQVSVOLT = 221.0
FREQVSCURR = 1.13
if not type(dfs) == list:
freqs = ((max_freq-PUMP_FREQUENCY)-(dfs.dropna()['tempV']-dfs.dropna()['tempV'].min())*FREQVSVOLT- (dfs.dropna()['currV']-dfs.dropna()['currV'].min())*FREQVSCURR)*xscale
dfs=[dfs]
plt.gcf().set_dpi(300)
for df in dfs:
df = df.dropna()
ax.errorbar(freqs,
df['ratio']*yscale,
yerr=df['ratioErr'],
fmt=fmt, mfc=mfc, color='black', ms=ms, **kwargs)
ax.set_ylabel(r'$\mathbf{\frac{V_{ss, cat}}{V_{ss}}} $ ', **labeldict)
ax.set_xlabel(r'$\Delta $ (GHz)', **labeldict)
if save_folder:
plt.savefig(os.path.join(save_folder, 'ratio_vs_freq.png'))
return freqs, ax
#return plt.gca(), plt.gcf()
#plt.show()
def plot_spline_fit(ax, x, y, s=1, yerr=None, color='black', scolor='black',figsize=(12,5), save_folder=None, title='',alpha=0.5,dpi=200, label='plot', fig=None,**kwargs):
from scipy.interpolate import splev, splrep
xnew = np.linspace(min(x), max(x), 3*len(x) )
y = [b for a,b in sorted(zip(x,y), key=lambda pair: pair[0])]
if yerr is not None:
yerr = [b for a,b in sorted(zip(x,yerr), key=lambda pair: pair[0])]
x = sorted(x)
spl = splrep(x, y, s=s)
ynew = splev(xnew, spl)
if fig is None:
plt.gcf().set_dpi(dpi)
plt.gcf().set_size_inches(figsize)
else:
fig.set_dpi(dpi)
fig.set_size_inches(figsize)
if yerr is not None:
ax.errorbar(x, y, yerr=yerr, fmt='o', color=color, **kwargs)
else:
ax.plot(x,y, 'o', **kwargs)
ax.plot(xnew, ynew, '-', color=scolor, alpha=alpha, label=label, **kwargs)
ax.set_ylabel(r'$\mathbf{\frac{V_{ss, cat}}{V_{ss}}} $ ')
ax.set_xlabel(r'$\Delta $ (GHz)')
ax.set_title(title, **titledict)
if save_folder:
plt.savefig(os.path.join(save_folder, 'spline_ratio_vs_freq.png'))
return ax
def plot_polyfit(x_data, y_data, spline_degree):
coefficients = np.polyfit(x_data, y_data, spline_degree)
x_interp = np.linspace(min(x_data), max(x_data), 100)
y_interp = np.polyval(coefficients, x_interp)
plt.scatter(x_data, y_data, label='Original Data')
plt.plot(x_interp, y_interp, label='Polynomial Interpolation (Degree={})'.format(spline_degree))
def collect_plots(source, destination, plot_name):
print(f'Collecting plots from {os.path.basename(source)}')
import shutil
os.makedirs(destination, exist_ok=True)
plot_files = []
for root, dirs, files in os.walk(source):
for file in files:
if file == plot_name:
plot_files.append(os.path.join(root, file))
for i, plot_file in enumerate(plot_files, start=0):
new_filename = f'{i}{plot_name}'
destination_path = os.path.join(destination, new_filename)
shutil.copy(plot_file, destination_path)
def create_GIF(images_folder, image_name):
import imageio
with imageio.get_writer(os.path.join(images_folder, f'{image_name}movie.gif'), mode='I', duration=0.5) as writer:
for filename in os.listdir(images_folder):
if image_name in filename:
image = imageio.imread(os.path.join(images_folder, filename))
writer.append_data(image)
def staircase_fit(data, peak_height=0.1, distance=100, data_offset=1, window_size=1, inc_final_peak=True):
def moving_average(arr, window_size):
weights = np.ones(window_size) / window_size
return np.convolve(arr, weights, mode='valid')
convdata1 = moving_average(data, window_size)
convdata2 = moving_average(data[data_offset+1:], window_size )
final = convdata1[:len(convdata2)]-convdata2
# plt.plot(data)
# plt.plot(convdata1)
# plt.plot(convdata2)
# plt.show()
# plt.plot(final)
from scipy.signal import find_peaks
x= final
peaks, _ = find_peaks(x, height=peak_height, distance=distance)
peaks = np.insert(peaks, 0, 0)
levels = []
plot_arr = []
for i, peak in enumerate(peaks):
if i < len(peaks) - 1:
temp = data[ peaks[i]:peaks[i+1] ]
plot_arr.extend( np.ones_like(temp)*np.mean(temp))
levels.append(np.mean(temp))
if inc_final_peak:
temp = data[peaks[-1]:]
levels.append(np.mean(temp))
plot_arr.extend( np.ones_like(temp)*np.mean(temp))
plt.plot(x)
plt.plot(peaks, x[peaks], "x")
plt.plot(np.zeros_like(x), "--", color="gray")
plt.show()
plt.plot(data)
plt.plot(np.ravel((plot_arr)))
plt.show()
plt.close()
plt.plot(np.array(levels)[np.where(abs(np.diff(levels))>0.05)[0]], 'o', ms=5)
return levels
def load_single_run(run_path):
filename = os.path.join(run_path, 'data.csv')
bkfilename = os.path.join(run_path, 'data_oldPD.csv')
settingsname = os.path.join(run_path, 'Settings.txt')
dh1 = MTdataHost(SAMPLE_RATE)
dh1.loadCATdata(fileName=filename, settingsName=settingsname)
return dh1
def load_mega_run(MEASURE_FOLDER, groupbyKey, titleKey, plot=True, save_plots=False, max_freq=384182.5, **kwargs):
# TODO: use the plot flag to do something?
df = get_data_frame(MEASURE_FOLDER,
**kwargs)
dfc= df.copy()
#df.dropna(inplace=True)
df_grouped = df.groupby(by=groupbyKey)
min_ratios = df_grouped['ratio'].min()
groups = dict(list(df_grouped))
dfs = [df for df in groups.values()]
# plotting ratio vs freq
max_freqs = [max_freq]*len(dfs)
zipped_data = list(zip(dfs, max_freqs))
fig1, ax = plt.subplots()
for i, (df, max_freq) in enumerate(zipped_data[:]):
data = df
freqs = ((max_freq-PUMP_FREQUENCY)-(data['tempV']-data['tempV'].min())*FREQVSVOLT- (data['currV']-data['currV'].min())*FREQVSCURR)
ax=plot_spline_fit(ax, x=freqs, y=data['ratio'], yerr=data['ratioErr'],scolor=f'C{i}', mfc=f'C{i}',color=f'C{i}', s=0.0, ms=5, figsize=(10, 10), label=f"{groupbyKey} = { data.iloc[10][groupbyKey] :.2f}", linewidth=2.5)
plt.legend()
plt.title(f'Loss Features, {titleKey} = {data[titleKey].mean():.2f}', **titledict)
plt.show()
#---------------------------------------------------
fig2 = plt.figure(2)
x = [df[groupbyKey].mean() for df in dfs]
y = [(df['ratio'].max() - df['ratio'].min()) for df in dfs]
plt.plot( x, y ,'-o')
plt.xlabel(groupbyKey)
plt.ylabel(r'SNR $ = V_{ss, off} - V_{ss, on}$ ')
plt.title(f'SNR Plot, {titleKey} = {df[titleKey].mean():.2f}', **titledict)
plt.show()
#---------------------------------------------------------
fig3=plt.figure(3)
for i, df in enumerate(dfs[:]):
data = df
freqs = ((384182.5-PUMP_FREQUENCY)-(data['tempV']-data['tempV'].min())*FREQVSVOLT- (data['currV']-data['currV'].min())*FREQVSCURR)
betaPAs = [a for a,b in sorted(zip(data['betaPA'], freqs), key=lambda pair:pair[1])]
freqs = sorted(freqs)
plt.plot(freqs, betaPAs, 'o-', ms=5, label=f"{groupbyKey}={data.iloc[10][groupbyKey]:.2f}")
plt.legend()
plt.xlabel(r'$\Delta$ (GHz)')
plt.ylabel(r'$\beta_{\mathrm{eff}}$ ')
plt.title(f'2-body Decay Plot, {titleKey} = {df[titleKey].mean():.2f}', **titledict)
plt.show()
if save_plots:
fig1.savefig(os.path.join(MEASURE_FOLDER, 'lossFeatures.png'))
fig2.savefig(join(MEASURE_FOLDER, 'SNRplot.png'), dpi=200)
fig3.savefig(join(MEASURE_FOLDER, 'betaVsFreq.png'), dpi=200)
return dfc
if __name__ == '__main__':
# run_path = r"C:\Users\svars\OneDrive\Desktop\UBC Lab\CATExperiment\CATMeasurements\testPArun9\16-53-10"
# filename = os.path.join(run_path, 'data.csv')
# bkfilename = os.path.join(run_path, 'data_oldPD.csv')
# settingsname = os.path.join(run_path, 'Settings.txt')
# dh1 = MTdataHost(SAMPLE_RATE)
# dh1.loadCATdata(fileName=filename, settingsName=settingsname)
# dh1.setAllCAT(0.002)
#dh1.CATbackgroundData(bkfilename)
pass
MEASURE_FOLDER = os.path.join(EXP_FOLDER, 'varPAamplRunOct9')
df = get_data_frame(MEASURE_FOLDER)
df.drop(columns='betaPAErr', inplace=True)
df.dropna(inplace=True)
dfc = df.copy()
df_grouped = df.groupby(by=['pump_reference', 'cat_AOM_ampl'])
groups = dict(list(df_grouped))
dfs = [df for df in groups.values()]
max_freqs = [384182.5]*len(dfs)
zipped_data = list(zip(dfs, max_freqs))
num1 = 5
fig1, ax1s = plt.subplots(num1) # detuning
fig1b, ax1sb = plt.subplots(num1)
fig1size = (8, num1*6)
fig1bsize= fig1size
fig1b.set_size_inches(fig1bsize)
num2 = 3
fig2, ax2s = plt.subplots(num2) # pump_reference
fig2b, ax2sb = plt.subplots(num2)
fig2size = (8, num2*6)
fig2bsize= fig2size
fig2b.set_size_inches(fig2bsize)
for i, (df, max_freq) in enumerate(zipped_data[:]):
j1 = i%num1
j2 = i//num1
data = df.dropna()
data = data[data['ratio'] < 1.3]
freqs = ((max_freq-PUMP_FREQUENCY)-(data['tempV']-data['tempV'].min())*FREQVSVOLT- (data['currV']-0.0)*FREQVSCURR)
pref = data['pump_reference'].mean()
detuning = data['cat_AOM_ampl'].mean()
print(pref, detuning)
ax1s[j1] = plot_spline_fit(ax1s[j1],
x=freqs, y=data['ratio'], yerr=data['ratioErr']
,scolor=f'C{j2}', mfc=f'C{j2}',color=f'C{j2}'
,s=0.0, ms=5,linewidth=1.5, figsize=fig1size
,label=f"Pump Amplitude = {pref:.2f}", fig=fig1)
ax1s[j1].set_title(f"Detuning = {detuning:.2f}", **titledict)
ax1s[j1].legend()
ax2s[j2] = plot_spline_fit(ax2s[j2],
x=freqs, y=data['ratio'], yerr=data['ratioErr'],
scolor=f'C{j1}', mfc=f'C{j1}',color=f'C{j1}',
s=0.0, ms=5, linewidth=1.5, figsize=fig2size,
label=f"Detuning = {detuning:.2f}", fig=fig2)
ax2s[j2].set_title(f"Pump Amplitude = { pref:.2f}", **titledict)
ax2s[j2].legend()
betaPAs = [a for a,b in sorted(zip(data['betaPA'], freqs), key=lambda pair:pair[1])]
freqs = sorted(freqs)
ax1sb[j1].plot(freqs, betaPAs, 'o-', color=f'C{j2}', ms=5, label=f"Pump Amplitude = {pref:.2f}")
ax1sb[j1].set_xlabel(r'$\Delta$ (GHz)')
ax1sb[j1].set_ylabel(r'$\beta_{\mathrm{eff}}$ ')
ax1sb[j1].legend()
ax1sb[j1].set_title(f"Detuning = {detuning:.2f}", **titledict)
ax2sb[j2].plot(freqs, betaPAs, 'o-',color=f'C{j1}', ms=5, label=f"Detuning = {detuning:.2f}")
ax2sb[j2].set_xlabel(r'$\Delta$ (GHz)')
ax2sb[j2].set_ylabel(r'$\beta_{\mathrm{eff}}$ ')
ax2sb[j2].legend()
ax2sb[j2].set_title(f"Pump Amplitude = { pref:.2f}", **titledict)
fig1.tight_layout()
fig1b.tight_layout()
fig2.tight_layout()
fig2b.tight_layout()
fig1.savefig(os.path.join(MEASURE_FOLDER, 'lossFeaturesDet.png'))
plt.show()
plt.close()
fig1b.savefig(os.path.join(MEASURE_FOLDER, '2bodyDet.png'))
plt.show()
plt.close()
fig2.savefig(os.path.join(MEASURE_FOLDER, 'lossFeaturesPampl.png'))
plt.show()
plt.close()
fig2b.savefig(os.path.join(MEASURE_FOLDER, '2BodyPampl.png'))
plt.show()
plt.close()
SNRdata = df_grouped['ratio'].max() - df_grouped['ratio'].min()
SNRdf = SNRdata.reset_index()
SNRdf.columns = ['pump_reference', 'pump_AOM_freq', 'SNR']
pivot_table = SNRdf.pivot('pump_reference', 'pump_AOM_freq', 'SNR')
xticklabels = [f'{180-2*x:.2f}' for x in pivot_table.columns]
yticklabels = [f'{y:.2f}' for y in pivot_table.index]
sns.heatmap(pivot_table, annot=True, fmt='.2f', xticklabels=xticklabels, yticklabels=yticklabels)
plt.xlabel("Detuning (MHz)")
plt.ylabel("Pump Reference")
plt.grid()
plt.savefig(os.path.join(MEASURE_FOLDER, 'heatmap.png'))
plt.show()
plt.close()
100%|██████████| 301/301 [11:16<00:00, 2.25s/it]
0.4000000000000001 0.5 0.4000000000000001 1.0 0.4000000000000001 2.0 0.7625 0.5 0.7625 1.0 0.7625 2.0 1.125 0.5 1.125 1.0 1.125 2.0 1.4875000000000003 0.5 1.4875000000000003 1.0 1.4875000000000003 2.0 1.8500000000000003 0.5 1.8500000000000003 1.0 1.8500000000000003 2.0
<ipython-input-2-5278889ac008>:96: FutureWarning: In a future version of pandas all arguments of DataFrame.pivot will be keyword-only.
pivot_table = SNRdf.pivot('pump_reference', 'pump_AOM_freq', 'SNR')
MEASURE_FOLDER = os.path.join(EXP_FOLDER, 'varPAamplRunOct9')
df = get_data_frame(MEASURE_FOLDER)
df.drop(columns='betaPAErr', inplace=True)
df.dropna(inplace=True)
dfc = df.copy()
df_grouped = df.groupby(by=['pump_reference', 'cat_AOM_ampl'])
groups = dict(list(df_grouped))
dfs = [df for df in groups.values()]
max_freqs = [384182.5]*len(dfs)
zipped_data = list(zip(dfs, max_freqs))
num1 = 5
fig1, ax1s = plt.subplots(num1) # detuning
fig1b, ax1sb = plt.subplots(num1)
fig1size = (8, num1*6)
fig1bsize= fig1size
fig1b.set_size_inches(fig1bsize)
num2 = 3
fig2, ax2s = plt.subplots(num2) # pump_reference
fig2b, ax2sb = plt.subplots(num2)
fig2size = (8, num2*6)
fig2bsize= fig2size
fig2b.set_size_inches(fig2bsize)
for i, (df, max_freq) in enumerate(zipped_data[:]):
j1 = i%num1
j2 = i//num1
data = df.dropna()
data = data[data['ratio'] < 1.3]
freqs = ((max_freq-PUMP_FREQUENCY)-(data['tempV']-data['tempV'].min())*FREQVSVOLT- (data['currV']-0.0)*FREQVSCURR)
pref = data['pump_reference'].mean()
detuning = data['cat_AOM_ampl'].mean()
print(pref, detuning)
ax1s[j1] = plot_spline_fit(ax1s[j1],
x=freqs, y=data['ratio'], yerr=data['ratioErr']
,scolor=f'C{j2}', mfc=f'C{j2}',color=f'C{j2}'
,s=0.0, ms=5,linewidth=1.5, figsize=fig1size
,label=f"Pump Amplitude = {pref:.2f}", fig=fig1)
ax1s[j1].set_title(f"Detuning = {detuning:.2f}", **titledict)
ax1s[j1].legend()
ax2s[j2] = plot_spline_fit(ax2s[j2],
x=freqs, y=data['ratio'], yerr=data['ratioErr'],
scolor=f'C{j1}', mfc=f'C{j1}',color=f'C{j1}',
s=0.0, ms=5, linewidth=1.5, figsize=fig2size,
label=f"Detuning = {detuning:.2f}", fig=fig2)
ax2s[j2].set_title(f"Pump Amplitude = { pref:.2f}", **titledict)
ax2s[j2].legend()
betaPAs = [a for a,b in sorted(zip(data['betaPA'], freqs), key=lambda pair:pair[1])]
freqs = sorted(freqs)
ax1sb[j1].plot(freqs, betaPAs, 'o-', color=f'C{j2}', ms=5, label=f"Pump Amplitude = {pref:.2f}")
ax1sb[j1].set_xlabel(r'$\Delta$ (GHz)')
ax1sb[j1].set_ylabel(r'$\beta_{\mathrm{eff}}$ ')
ax1sb[j1].legend()
ax1sb[j1].set_title(f"Detuning = {detuning:.2f}", **titledict)
ax2sb[j2].plot(freqs, betaPAs, 'o-',color=f'C{j1}', ms=5, label=f"Detuning = {detuning:.2f}")
ax2sb[j2].set_xlabel(r'$\Delta$ (GHz)')
ax2sb[j2].set_ylabel(r'$\beta_{\mathrm{eff}}$ ')
ax2sb[j2].legend()
ax2sb[j2].set_title(f"Pump Amplitude = { pref:.2f}", **titledict)
fig1.tight_layout()
fig1b.tight_layout()
fig2.tight_layout()
fig2b.tight_layout()
fig1.savefig(os.path.join(MEASURE_FOLDER, 'lossFeaturesDet.png'))
plt.show()
plt.close()
# fig1b.savefig(os.path.join(MEASURE_FOLDER, '2bodyDet.png'))
# plt.show()
# plt.close()
fig2.savefig(os.path.join(MEASURE_FOLDER, 'lossFeaturesPampl.png'))
plt.show()
plt.close()
# fig2b.savefig(os.path.join(MEASURE_FOLDER, '2BodyPampl.png'))
# plt.show()
# plt.close()
100%|██████████| 306/306 [00:05<00:00, 59.80it/s]
0.4000000000000001 0.5 0.4000000000000001 1.0 0.4000000000000001 2.0 0.7625 0.5 0.7625 1.0 0.7625 2.0 1.125 0.5 1.125 1.0 1.125 2.0 1.4875000000000003 0.5 1.4875000000000003 1.0 1.4875000000000003 2.0 1.8500000000000003 0.5 1.8500000000000003 1.0 1.8500000000000003 2.0
MEASURE_FOLDER = os.path.join(EXP_FOLDER, 'varPAamplRunOct9')
df = get_data_frame(MEASURE_FOLDER)
df.drop(columns='betaPAErr', inplace=True)
df.dropna(inplace=True)
dfc = df.copy()
df_grouped = df.groupby(by=['cat_AOM_ampl', 'pump_reference'])
groups = dict(list(df_grouped))
dfs = [df for df in groups.values()]
max_freqs = [384182.5]*len(dfs)
zipped_data = list(zip(dfs, max_freqs))
num1 = 3
fig1, ax1s = plt.subplots(num1) # detuning
fig1b, ax1sb = plt.subplots(num1)
fig1size = (8, num1*6)
fig1bsize= fig1size
fig1b.set_size_inches(fig1bsize)
num2 = 5
fig2, ax2s = plt.subplots(num2) # pump_reference
fig2b, ax2sb = plt.subplots(num2)
fig2size = (8, num2*6)
fig2bsize= fig2size
fig2b.set_size_inches(fig2bsize)
for i, (df, max_freq) in enumerate(zipped_data[:]):
j1 = i%num1
j2 = i//num1
data = df.dropna()
data = data[data['ratio'] < 1.3]
freqs = ((max_freq-PUMP_FREQUENCY)-(data['tempV']-data['tempV'].min())*FREQVSVOLT- (data['currV']-0.0)*FREQVSCURR)
pref = data['pump_reference'].mean()
detuning = data['cat_AOM_ampl'].mean()
print(pref, detuning)
ax1s[j1] = plot_spline_fit(ax1s[j1],
x=freqs, y=data['ratio'], yerr=data['ratioErr']
,scolor=f'C{j2}', mfc=f'C{j2}',color=f'C{j2}'
,s=0.0, ms=5,linewidth=1.5, figsize=fig1size
,label=f"Pump Amplitude = {pref:.2f}", fig=fig1)
ax1s[j1].set_title(f"Detuning = {detuning:.2f}", **titledict)
ax1s[j1].legend()
ax2s[j2] = plot_spline_fit(ax2s[j2],
x=freqs, y=data['ratio'], yerr=data['ratioErr'],
scolor=f'C{j1}', mfc=f'C{j1}',color=f'C{j1}',
s=0.0, ms=5, linewidth=1.5, figsize=fig2size,
label=f"Detuning = {detuning:.2f}", fig=fig2)
ax2s[j2].set_title(f"Pump Amplitude = { pref:.2f}", **titledict)
ax2s[j2].legend()
# betaPAs = [a for a,b in sorted(zip(data['betaPA'], freqs), key=lambda pair:pair[1])]
# freqs = sorted(freqs)
# ax1sb[j1].plot(freqs, betaPAs, 'o-', color=f'C{j2}', ms=5, label=f"Pump Amplitude = {pref:.2f}")
# ax1sb[j1].set_xlabel(r'$\Delta$ (GHz)')
# ax1sb[j1].set_ylabel(r'$\beta_{\mathrm{eff}}$ ')
# ax1sb[j1].legend()
# ax1sb[j1].set_title(f"Detuning = {detuning:.2f}", **titledict)
# ax2sb[j2].plot(freqs, betaPAs, 'o-',color=f'C{j1}', ms=5, label=f"Detuning = {detuning:.2f}")
# ax2sb[j2].set_xlabel(r'$\Delta$ (GHz)')
# ax2sb[j2].set_ylabel(r'$\beta_{\mathrm{eff}}$ ')
# ax2sb[j2].legend()
# ax2sb[j2].set_title(f"Pump Amplitude = { pref:.2f}", **titledict)
fig1.tight_layout()
fig1b.tight_layout()
fig2.tight_layout()
fig2b.tight_layout()
fig1.savefig(os.path.join(MEASURE_FOLDER, 'lossFeaturesDet.png'))
plt.show()
plt.close()
# fig1b.savefig(os.path.join(MEASURE_FOLDER, '2bodyDet.png'))
# plt.show()
# plt.close()
fig2.savefig(os.path.join(MEASURE_FOLDER, 'lossFeaturesPampl.png'))
plt.show()
plt.close()
# fig2b.savefig(os.path.join(MEASURE_FOLDER, '2BodyPampl.png'))
# plt.show()
# plt.close()
100%|██████████| 306/306 [00:03<00:00, 84.99it/s]
0.4000000000000001 0.5 0.7625 0.5 1.125 0.5 1.4875000000000003 0.5 1.8500000000000003 0.5 0.4000000000000001 1.0 0.7625 1.0 1.125 1.0 1.4875000000000003 1.0 1.8500000000000003 1.0 0.4000000000000001 2.0 0.7625 2.0 1.125 2.0 1.4875000000000003 2.0 1.8500000000000003 2.0
MEASURE_FOLDER = os.path.join(EXP_FOLDER, 'varPAamplRunOct9')
df = get_data_frame(MEASURE_FOLDER)
df.drop(columns='betaPAErr', inplace=True)
df.dropna(inplace=True)
dfc = df.copy()
df_grouped = df.groupby(by=['cat_AOM_ampl', 'pump_reference'])
groups = dict(list(df_grouped))
dfs = [df for df in groups.values()]
max_freqs = [384182.5]*len(dfs)
zipped_data = list(zip(dfs, max_freqs))
num1 = 3
fig1, ax1s = plt.subplots(num1) # detuning
fig1b, ax1sb = plt.subplots(num1)
fig1size = (8, num1*6)
fig1bsize= fig1size
fig1b.set_size_inches(fig1bsize)
num2 = 5
fig2, ax2s = plt.subplots(num2) # pump_reference
fig2b, ax2sb = plt.subplots(num2)
fig2size = (8, num2*6)
fig2bsize= fig2size
fig2b.set_size_inches(fig2bsize)
for i, (df, max_freq) in enumerate(zipped_data[:]):
j1 = i%num2
j2 = i//num2
data = df.dropna()
data = data[data['ratio'] < 1.3]
freqs = ((max_freq-PUMP_FREQUENCY)-(data['tempV']-data['tempV'].min())*FREQVSVOLT- (data['currV']-0.0)*FREQVSCURR)
pref = data['pump_reference'].mean()
detuning = data['cat_AOM_ampl'].mean()
print(pref, detuning)
ax1s[j1] = plot_spline_fit(ax1s[j1],
x=freqs, y=data['ratio'], yerr=data['ratioErr']
,scolor=f'C{j2}', mfc=f'C{j2}',color=f'C{j2}'
,s=0.0, ms=5,linewidth=1.5, figsize=fig1size
,label=f"Pump Amplitude = {pref:.2f}", fig=fig1)
ax1s[j1].set_title(f"Detuning = {detuning:.2f}", **titledict)
ax1s[j1].legend()
ax2s[j2] = plot_spline_fit(ax2s[j2],
x=freqs, y=data['ratio'], yerr=data['ratioErr'],
scolor=f'C{j1}', mfc=f'C{j1}',color=f'C{j1}',
s=0.0, ms=5, linewidth=1.5, figsize=fig2size,
label=f"Detuning = {detuning:.2f}", fig=fig2)
ax2s[j2].set_title(f"Pump Amplitude = { pref:.2f}", **titledict)
ax2s[j2].legend()
# betaPAs = [a for a,b in sorted(zip(data['betaPA'], freqs), key=lambda pair:pair[1])]
# freqs = sorted(freqs)
# ax1sb[j1].plot(freqs, betaPAs, 'o-', color=f'C{j2}', ms=5, label=f"Pump Amplitude = {pref:.2f}")
# ax1sb[j1].set_xlabel(r'$\Delta$ (GHz)')
# ax1sb[j1].set_ylabel(r'$\beta_{\mathrm{eff}}$ ')
# ax1sb[j1].legend()
# ax1sb[j1].set_title(f"Detuning = {detuning:.2f}", **titledict)
# ax2sb[j2].plot(freqs, betaPAs, 'o-',color=f'C{j1}', ms=5, label=f"Detuning = {detuning:.2f}")
# ax2sb[j2].set_xlabel(r'$\Delta$ (GHz)')
# ax2sb[j2].set_ylabel(r'$\beta_{\mathrm{eff}}$ ')
# ax2sb[j2].legend()
# ax2sb[j2].set_title(f"Pump Amplitude = { pref:.2f}", **titledict)
fig1.tight_layout()
fig1b.tight_layout()
fig2.tight_layout()
fig2b.tight_layout()
fig1.savefig(os.path.join(MEASURE_FOLDER, 'lossFeaturesDet.png'))
plt.show()
plt.close()
# fig1b.savefig(os.path.join(MEASURE_FOLDER, '2bodyDet.png'))
# plt.show()
# plt.close()
fig2.savefig(os.path.join(MEASURE_FOLDER, 'lossFeaturesPampl.png'))
plt.show()
plt.close()
# fig2b.savefig(os.path.join(MEASURE_FOLDER, '2BodyPampl.png'))
# plt.show()
# plt.close()
100%|██████████| 306/306 [00:03<00:00, 93.43it/s]
0.4000000000000001 0.5 0.7625 0.5 1.125 0.5 1.4875000000000003 0.5
--------------------------------------------------------------------------- IndexError Traceback (most recent call last) Cell In[5], line 40 36 detuning = data['cat_AOM_ampl'].mean() 37 print(pref, detuning) ---> 40 ax1s[j1] = plot_spline_fit(ax1s[j1], 41 x=freqs, y=data['ratio'], yerr=data['ratioErr'] 42 ,scolor=f'C{j2}', mfc=f'C{j2}',color=f'C{j2}' 43 ,s=0.0, ms=5,linewidth=1.5, figsize=fig1size 44 ,label=f"Pump Amplitude = {pref:.2f}", fig=fig1) 46 ax1s[j1].set_title(f"Detuning = {detuning:.2f}", **titledict) 47 ax1s[j1].legend() IndexError: index 3 is out of bounds for axis 0 with size 3
MEASURE_FOLDER = os.path.join(EXP_FOLDER, 'varPAamplRunOct9')
df = get_data_frame(MEASURE_FOLDER)
df.drop(columns='betaPAErr', inplace=True)
df.dropna(inplace=True)
dfc = df.copy()
df_grouped = df.groupby(by=['cat_AOM_ampl', 'pump_reference'])
groups = dict(list(df_grouped))
dfs = [df for df in groups.values()]
max_freqs = [384182.5]*len(dfs)
zipped_data = list(zip(dfs, max_freqs))
num1 = 3
fig1, ax1s = plt.subplots(num1) # detuning
fig1b, ax1sb = plt.subplots(num1)
fig1size = (8, num1*6)
fig1bsize= fig1size
fig1b.set_size_inches(fig1bsize)
num2 = 5
fig2, ax2s = plt.subplots(num2) # pump_reference
fig2b, ax2sb = plt.subplots(num2)
fig2size = (8, num2*6)
fig2bsize= fig2size
fig2b.set_size_inches(fig2bsize)
for i, (df, max_freq) in enumerate(zipped_data[:]):
j1 = i//num1
j2 = i%num1
data = df.dropna()
data = data[data['ratio'] < 1.3]
freqs = ((max_freq-PUMP_FREQUENCY)-(data['tempV']-data['tempV'].min())*FREQVSVOLT- (data['currV']-0.0)*FREQVSCURR)
pref = data['pump_reference'].mean()
detuning = data['cat_AOM_ampl'].mean()
print(pref, detuning)
ax1s[j1] = plot_spline_fit(ax1s[j1],
x=freqs, y=data['ratio'], yerr=data['ratioErr']
,scolor=f'C{j2}', mfc=f'C{j2}',color=f'C{j2}'
,s=0.0, ms=5,linewidth=1.5, figsize=fig1size
,label=f"Pump Amplitude = {pref:.2f}", fig=fig1)
ax1s[j1].set_title(f"Detuning = {detuning:.2f}", **titledict)
ax1s[j1].legend()
ax2s[j2] = plot_spline_fit(ax2s[j2],
x=freqs, y=data['ratio'], yerr=data['ratioErr'],
scolor=f'C{j1}', mfc=f'C{j1}',color=f'C{j1}',
s=0.0, ms=5, linewidth=1.5, figsize=fig2size,
label=f"Detuning = {detuning:.2f}", fig=fig2)
ax2s[j2].set_title(f"Pump Amplitude = { pref:.2f}", **titledict)
ax2s[j2].legend()
# betaPAs = [a for a,b in sorted(zip(data['betaPA'], freqs), key=lambda pair:pair[1])]
# freqs = sorted(freqs)
# ax1sb[j1].plot(freqs, betaPAs, 'o-', color=f'C{j2}', ms=5, label=f"Pump Amplitude = {pref:.2f}")
# ax1sb[j1].set_xlabel(r'$\Delta$ (GHz)')
# ax1sb[j1].set_ylabel(r'$\beta_{\mathrm{eff}}$ ')
# ax1sb[j1].legend()
# ax1sb[j1].set_title(f"Detuning = {detuning:.2f}", **titledict)
# ax2sb[j2].plot(freqs, betaPAs, 'o-',color=f'C{j1}', ms=5, label=f"Detuning = {detuning:.2f}")
# ax2sb[j2].set_xlabel(r'$\Delta$ (GHz)')
# ax2sb[j2].set_ylabel(r'$\beta_{\mathrm{eff}}$ ')
# ax2sb[j2].legend()
# ax2sb[j2].set_title(f"Pump Amplitude = { pref:.2f}", **titledict)
fig1.tight_layout()
fig1b.tight_layout()
fig2.tight_layout()
fig2b.tight_layout()
fig1.savefig(os.path.join(MEASURE_FOLDER, 'lossFeaturesDet.png'))
plt.show()
plt.close()
# fig1b.savefig(os.path.join(MEASURE_FOLDER, '2bodyDet.png'))
# plt.show()
# plt.close()
fig2.savefig(os.path.join(MEASURE_FOLDER, 'lossFeaturesPampl.png'))
plt.show()
plt.close()
# fig2b.savefig(os.path.join(MEASURE_FOLDER, '2BodyPampl.png'))
# plt.show()
# plt.close()
100%|██████████| 306/306 [00:03<00:00, 92.77it/s]
0.4000000000000001 0.5 0.7625 0.5 1.125 0.5 1.4875000000000003 0.5 1.8500000000000003 0.5 0.4000000000000001 1.0 0.7625 1.0 1.125 1.0 1.4875000000000003 1.0 1.8500000000000003 1.0
--------------------------------------------------------------------------- IndexError Traceback (most recent call last) Cell In[6], line 40 36 detuning = data['cat_AOM_ampl'].mean() 37 print(pref, detuning) ---> 40 ax1s[j1] = plot_spline_fit(ax1s[j1], 41 x=freqs, y=data['ratio'], yerr=data['ratioErr'] 42 ,scolor=f'C{j2}', mfc=f'C{j2}',color=f'C{j2}' 43 ,s=0.0, ms=5,linewidth=1.5, figsize=fig1size 44 ,label=f"Pump Amplitude = {pref:.2f}", fig=fig1) 46 ax1s[j1].set_title(f"Detuning = {detuning:.2f}", **titledict) 47 ax1s[j1].legend() IndexError: index 3 is out of bounds for axis 0 with size 3
MEASURE_FOLDER = os.path.join(EXP_FOLDER, 'varPAamplRunOct9')
df = get_data_frame(MEASURE_FOLDER)
df.drop(columns='betaPAErr', inplace=True)
df.dropna(inplace=True)
dfc = df.copy()
df_grouped = df.groupby(by=['cat_AOM_ampl', 'pump_reference'])
groups = dict(list(df_grouped))
dfs = [df for df in groups.values()]
max_freqs = [384182.5]*len(dfs)
zipped_data = list(zip(dfs, max_freqs))
num1 = 3
fig1, ax1s = plt.subplots(num1) # detuning
fig1b, ax1sb = plt.subplots(num1)
fig1size = (8, num1*6)
fig1bsize= fig1size
fig1b.set_size_inches(fig1bsize)
num2 = 5
fig2, ax2s = plt.subplots(num2) # pump_reference
fig2b, ax2sb = plt.subplots(num2)
fig2size = (8, num2*6)
fig2bsize= fig2size
fig2b.set_size_inches(fig2bsize)
for i, (df, max_freq) in enumerate(zipped_data[:]):
j1 = i//num2
j2 = i%num2
data = df.dropna()
data = data[data['ratio'] < 1.3]
freqs = ((max_freq-PUMP_FREQUENCY)-(data['tempV']-data['tempV'].min())*FREQVSVOLT- (data['currV']-0.0)*FREQVSCURR)
pref = data['pump_reference'].mean()
detuning = data['cat_AOM_ampl'].mean()
print(pref, detuning)
ax1s[j1] = plot_spline_fit(ax1s[j1],
x=freqs, y=data['ratio'], yerr=data['ratioErr']
,scolor=f'C{j2}', mfc=f'C{j2}',color=f'C{j2}'
,s=0.0, ms=5,linewidth=1.5, figsize=fig1size
,label=f"Pump Amplitude = {pref:.2f}", fig=fig1)
ax1s[j1].set_title(f"Detuning = {detuning:.2f}", **titledict)
ax1s[j1].legend()
ax2s[j2] = plot_spline_fit(ax2s[j2],
x=freqs, y=data['ratio'], yerr=data['ratioErr'],
scolor=f'C{j1}', mfc=f'C{j1}',color=f'C{j1}',
s=0.0, ms=5, linewidth=1.5, figsize=fig2size,
label=f"Detuning = {detuning:.2f}", fig=fig2)
ax2s[j2].set_title(f"Pump Amplitude = { pref:.2f}", **titledict)
ax2s[j2].legend()
# betaPAs = [a for a,b in sorted(zip(data['betaPA'], freqs), key=lambda pair:pair[1])]
# freqs = sorted(freqs)
# ax1sb[j1].plot(freqs, betaPAs, 'o-', color=f'C{j2}', ms=5, label=f"Pump Amplitude = {pref:.2f}")
# ax1sb[j1].set_xlabel(r'$\Delta$ (GHz)')
# ax1sb[j1].set_ylabel(r'$\beta_{\mathrm{eff}}$ ')
# ax1sb[j1].legend()
# ax1sb[j1].set_title(f"Detuning = {detuning:.2f}", **titledict)
# ax2sb[j2].plot(freqs, betaPAs, 'o-',color=f'C{j1}', ms=5, label=f"Detuning = {detuning:.2f}")
# ax2sb[j2].set_xlabel(r'$\Delta$ (GHz)')
# ax2sb[j2].set_ylabel(r'$\beta_{\mathrm{eff}}$ ')
# ax2sb[j2].legend()
# ax2sb[j2].set_title(f"Pump Amplitude = { pref:.2f}", **titledict)
fig1.tight_layout()
fig1b.tight_layout()
fig2.tight_layout()
fig2b.tight_layout()
fig1.savefig(os.path.join(MEASURE_FOLDER, 'lossFeaturesDet.png'))
plt.show()
plt.close()
# fig1b.savefig(os.path.join(MEASURE_FOLDER, '2bodyDet.png'))
# plt.show()
# plt.close()
fig2.savefig(os.path.join(MEASURE_FOLDER, 'lossFeaturesPampl.png'))
plt.show()
plt.close()
# fig2b.savefig(os.path.join(MEASURE_FOLDER, '2BodyPampl.png'))
# plt.show()
# plt.close()
100%|██████████| 306/306 [00:03<00:00, 93.46it/s]
0.4000000000000001 0.5 0.7625 0.5 1.125 0.5 1.4875000000000003 0.5 1.8500000000000003 0.5 0.4000000000000001 1.0 0.7625 1.0 1.125 1.0 1.4875000000000003 1.0 1.8500000000000003 1.0 0.4000000000000001 2.0 0.7625 2.0 1.125 2.0 1.4875000000000003 2.0 1.8500000000000003 2.0